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Distributed Neural Interfaces: Challenges and Trends in Scaling Implantable Technology

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Handbook of Neuroengineering

Abstract

Current implantable neural interfaces, both clinically available solutions and research tools, rely on a limited number of implanted devices (from one to few units). This factor, aside from the obvious spatial resolution limitations, does not conform to the paradigm of the brain as a massively parallel computational system and creates a bottleneck in the amount of information that could be exchanged between the brain and an external processing unit. This issue has fuelled recent research efforts towards the study of distributed neural interfaces, systems that depend on a network of implanted nodes. Such configuration allows to spread of the overall complexity across multiple devices, which can now be more easily scaled down in size and individual power consumption, improving their conformity with the surrounding tissue, which is a major concern in current monolithic solutions. However, this architecture brings a new set of challenges ranging from the optimization of ultra-low-power electronics, through the formulation of a wireless transmission scheme for efficient power delivery and data transfer to the investigation of novel materials and methods for the fabrication of micro-scale, long-term reliable implants. This chapter outlines state of the art and describes design considerations for the future autonomous, wireless distributed neural implants. Aspects of miniaturization and chronic stability of devices including materials choice, implantation procedure, packaging strategies and microelectrode types are described, alongside a discussion on different modalities to achieve wireless power transfer and data telemetry.

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Authors and Affiliations

  1. Imperial College London, London, UK

    Katarzyna M. Szostak, Peilong Feng, Federico Mazza & Timothy G. Constandinou

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  2. Peilong Feng
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  3. Federico Mazza
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  4. Timothy G. Constandinou
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Correspondence to Timothy G. Constandinou .

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  1. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore

    Nitish V. Thakor

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  1. National University of Singapore, Singapore, Singapore

    Nuan Chen Ph.D

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Szostak, K.M., Feng, P., Mazza, F., Constandinou, T.G. (2021). Distributed Neural Interfaces: Challenges and Trends in Scaling Implantable Technology. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_11-1

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